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16th International Conference on Interfaces and Human computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022

作者： Mkpojiogu, Emmanuel O.C. Okeke-Uzodike, Obianuju E. Omopariola, Victor School of Computing Universiti Utara Malaysia UUM Sintok06010 Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology KwaZulu-Natal Pietermaritzburg South Africa Department of Computer and Information Technology Veritas University Abuja Nigeria

ISBN: (纸本)9789898704405

The purpose of this study was to conduct a literature review in order to gather material that would serve to underpin and support a link between UX requirements and psychological theories. In the paper, relevant sources from extant literature were accessed and synthesized. The outcome revealed that psychological theories support user experience needs. UX needs as psychological needs are fully supported by psychological theories. In the light of this, the search for ways to meet these needs should be done using psychological needs fulfillment strategies. Further studies will investigate ways of meeting these user experience needs and how these needs can be modeled for UX design and evaluation. © 2022 International Conference on Interfaces and Human computer Interaction

关键词： Human computer interaction

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Designing and simulating IoT environments by using a model-driven approach*

Designing and simulating IoT environments by using a model-d...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： José A. Barriga Pedro J. Clemente Department of Computer and Telematic Systems Engineering Quercus Software Engineering Group Universidad de Extremadura C&#x00E1 ceres Spain

Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. However, in order to decrease the cost associated to develop and test the IoT system it can be previously simulated. In this regard, designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Model-driven development aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach is applied to define, generate code and deploy IoT systems simulations. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics. Additionally, a case study, focused on an Industrial IoT environment is presented to show the simulation expressiveness.

关键词： Analytical models Codes Focusing computer architecture Sensor phenomena and characterization software systems Systems simulation

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IMMAT: Mesh Reconstruction from Single View Images by Medial Axis Transform Prediction

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CAD computer Aided Design 2022年 150卷 1页

作者： Hu, Jianwei Chen, Gang Yang, Baorong Wang, Ningna Guo, Xiaohu Wang, Bin BNRist and School of Software Tsinghua University China College of Computer Engineering Jimei University China Department of Computer Science The University of Texas at Dallas United States

The representation of a 3D shape is a key element for capturing the overall structure as well as the local details. In this paper, we propose to predict a mesh representation of the Medial Axis Transform (called medial mesh) as an intermediate representation with our IMMAT framework, to reconstruct the 3D shape from a single view image. Because the MAT contains the skeleton topology and local thickness information, it not only enhances the ability to reconstruct topologically complex shapes but also better preserves the local details with its thickness control. The framework consists of three modules. The Image2Sphere module predicts the medial spheres inside the shape surface and the Topology Prediction module predicts the topological relationship (skeleton) between the predicted spheres. Then the MAT Smoothing module smooths the predicted MAT and fine-tunes the coordinates and radii of the spheres by graph convolution. The final 3D surface can be reconstructed by converting the predicted MAT to an implicit surface through CSG operation and then extracting the boundary surface through Marching Cubes. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively on the reconstruction task. © 2022 Elsevier Ltd

关键词： Forecasting

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METHODS FOR THE EVALUATION OF UX OVER TIME 16

METHODS FOR THE EVALUATION OF UX OVER TIME

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ISBN: (纸本)9789898704405

In this paper a literature review strategy was utilized to assess the methods of evaluating UX longitudinally. The review revealed that studies on user experience evaluation over time are rare. This rareness is attributed to the cost involved in carrying out such studies. This research nonetheless suggests that lack of sufficient information and adequate knowledge on methods used for the evaluation of UX over time may also be the reason for the scarcity of studies on long-term user experience evaluation. On this note, this paper reviewed previous works on long-term evaluation of UX to elicit the methods employed in conducting such evaluation to inform possible researchers on the existing methods. The outcome of the study indicated a number of methods for the evaluation of UX over time. © 2022 International Conference on Interfaces and Human computer Interaction

关键词： Human computer interaction

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Domain Adaptive Code Completion via Language Models and Deco...

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IEEE International Conference on Automated software engineering (ASE)

作者： Ze Tang Jidong Ge Shangqing Liu Tingwei Zhu Tongtong Xu Liguo Huang Bin Luo National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University USA

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$ NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$ NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$ NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network

The New Frontier in Media: AI-Driven Content Creation for Ad...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Shashishekhar Ramagundam Niharika Karne Software Development & Engineering in Content Discovery & AI Comcast USA AL. Department of Computer Science Masters in Computer Science Troy University Montgomery Campus USA Sr. Principal Systems Engineer in C& RS Platform Services DT Amtrak USA Department of Electrical Engineering Masters in Electrical Engineering University of South Alabama USA

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics.

关键词： Productivity Measurement Deep learning Ethics TV Media Generative adversarial networks Artificial intelligence Informatics Faces

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Model Design and Exponential State Estimation for Discrete-Time Delayed Memristive Spiking Neural P Systems

SSRN

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SSRN 2024年

作者： Yang, Nijing Peng, Hong Wang, Jun Lu, Xiang Wang, Xiangxiang Yu, Yongbin School of Computer and Software Engineering Xihua University Chengdu610039 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China Ascend Computing Product Department Huawei Technologies Co Ltd Chengdu610041 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

This paper investigates the exponential state estimation of discrete-time memristive spiking neural P system (MSNPS). The spiking neural P system (SNPS) offers algorithmic support for neural morphology computation and AI chips, boasting advantages such as high performance and efficiency. As a new type of information device, memristors have efficient computing characteristics that integrate memory and computation, and can serve as synapses in SNPS. Hence, to harness the advantages of SNPS and memristors synergistically, this paper presents a pioneering MSNPS circuit model, in which memristors are used to replace resistors in SNPS. Meanwhile, MSNPS mathematical model is constructed based on circuit model. To be more practical, the time delays are analyzed in the system. Then, because of the discreteness of SNPS, the continuous MSNPS is discretized. Moreover, some sufficient conditions for exponential state estimation are established by utilizing Lyapunov functional to MSNPS. Finally, a numerical simulation example is constructed to validate the main findings. © 2024, The Authors. All rights reserved.

关键词： State estimation

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Clicks vs. bricks: Exploring the critical success factors for consumer purchase intention in e-commerce

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Procedia computer Science 2024年 239卷 590-597页

作者： Yuqiang Zhao Kathryn Cormican Suzana Sampaio Enterprise Research Centre and LERO—The Irish Software Research Centre School of Engineering University of Galway Ireland H91 TK33 Computer Science Department (DC) Federal Rural University of Pernambuco Recife PE Brazil

As more and more people shift their purchasing habits from bricks-and-mortar stores to online platforms, e-commerce practitioners must understand the factors that influence consumers’ purchase intentions. By identifying these key determinants, practitioners can develop effective strategies to attract and retain customers. This study aims to examine the impact of information quality, product quality, user satisfaction, system quality, and service quality on consumers’ purchase intentions in e-commerce. Empirical data were collected from 181 consumers to capture their perceptions of e-commerce, the factors influencing their purchase intentions, and the relationships between them. The data were then analyzed using Pearson’s correlations and structural equation modelling techniques to determine the importance of each factor. The results show that information quality, product quality, user satisfaction, system quality, and service quality significantly influence consumers’ purchase intentions in e-commerce. This study provides valuable insights into the factors that affect consumers’ purchase intentions in e-commerce and allows practitioners to develop appropriate strategies customized to their own context.

关键词： E-commerce consumer behavior purchase intentions structural equation modeling

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Optimizing Cloud IoT Applications with Machine Learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy Machine learning algorithms Scalability Internet of Things Security Anomaly detection

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Exploring Large Language Models for Natural Language Processing

Exploring Large Language Models for Natural Language Process...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Ayodeji Olalekan Salau Esuku Dickson Emmanuel Ahmed Alemran Chandra Kumar Dixit Sepiribo Lucky Braide Department of Electrical/Electronics and Computer Engineering College of Engineering Afe Babalola University AdoEkiti Nigeria Department of Software Engineering Medicine College Misan University Iraq Institute of Engineering and Technology Dr. Shakuntala Misra National Rehabilitation University Lucknow Uttar Pradesh India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Large language models have become increasingly popular in natural language processing due to their ability to generate high-quality text and perform various language-related tasks. However, there are growing concerns regarding the ethical implications of these models, particularly concerning potential misuse and bias. This paper delves into the advancements and limitations of large language models for Natural Language Processing (NLP), exploring their ability to improve language understanding and generation. The paper also examines the challenges these models face in terms of computational power and model interpretability. Finally, the paper provides recommendations for future research to address these concerns, ensuring the responsible development of large language models and their integration into the future of NLP and Artificial Intelligence (AI).

关键词： Economics Ethics Data privacy Large language models Computational modeling Biological system modeling Training data Natural language processing Security Faces

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